A highly conductive, robust, self-healable, and thermally responsive liquid metal-based hydrogel for reversible electrical switches.

This study introduces a thermally responsive smart hydrogel with enhanced electrical properties achieved through volume switching. This advancement was realized by incorporating multiscale liquid metal particles (LMPs) into the PNIPAM hydrogel during polymerization, using their inherent elasticity and conductivity when deswelled. Unlike traditional conductive additives, LMPs endow the PNIPAM hydrogel with a remarkably consistent volume switching ratio, significantly enhancing electrical switching. This is attributed to the minimal nucleation effect of LMPs during polymerization and their liquid-like behavior, like vacancies in the polymeric hydrogel under compression. The PNIPAM/LMP hydrogel exhibits the highest electrical switching, with an unprecedented switch of 6.1 orders of magnitude. Even after repeated swelling/deswelling cycles that merge some LMPs and increase the conductivity when swelled, the hydrogel consistently maintains an electrical switch exceeding 4.5 orders of magnitude, which is still the highest record to date. Comprehensive measurements reveal that the hydrogel possesses robust mechanical properties, a tissue-like compression modulus, biocompatibility, and self-healing capabilities. These features make the PNIPAM/LMP hydrogel an ideal candidate for long-term implantable bioelectronics, offering a solution to the mechanical mismatch with dynamic human tissues.

Controlled growth of perovskite layers with volatile alkylammonium chlorides.

Controlling the crystallinity and surface morphology of perovskite layers by methods such as solvent engineering1,2 and methylammonium chloride addition3-7 is an effective strategy for achieving high-efficiency perovskite solar cells. In particular, it is essential to deposit α-formamidinium lead iodide (FAPbI3) perovskite thin films with few defects due to their excellent crystallinity and large grain size. Here we report the controlled crystallization of perovskite thin films with the combination of alkylammonium chlorides (RACl) added to FAPbI3. The δ-phase to α-phase transition of FAPbI3 and the crystallization process and surface morphology of the perovskite thin films coated with RACl under various conditions were investigated through in situ grazing-incidence wide-angle X-ray diffraction and scanning electron microscopy. RACl added to the precursor solution was believed to be easily volatilized during coating and annealing owing to dissociation into RA0 and HCl with deprotonation of RA+ induced by RA⋯H+-Cl- binding to PbI2 in FAPbI3. Thus, the type and amount of RACl determined the δ-phase to α-phase transition rate, crystallinity, preferred orientation and surface morphology of the final α-FAPbI3. The resulting perovskite thin layers facilitated the fabrication of perovskite solar cells with a power-conversion efficiency of 26.08% (certified 25.73%) under standard illumination.

Recent Advances in Contrast-Enhanced Photoacoustic Imaging: Overcoming the Physical and Practical Challenges.

For decades now, photoacoustic imaging (PAI) has been investigated to realize its potential as a niche biomedical imaging modality. Despite its highly desirable optical contrast and ultrasonic spatiotemporal resolution, PAI is challenged by such physical limitations as a low signal-to-noise ratio (SNR), diminished image contrast due to strong optical attenuation, and a lower-bound on spatial resolution in deep tissue. In addition, contrast-enhanced PAI has faced practical limitations such as insufficient cell-specific targeting due to low delivery efficiency and difficulties in developing clinically translatable agents. Identifying these limitations is essential to the continuing expansion of the field, and substantial advances in developing contrast-enhancing agents, complemented by high-performance image acquisition systems, have synergistically dealt with the challenges of conventional PAI. This review covers the past four years of research on pushing the physical and practical challenges of PAI in terms of SNR/contrast, spatial resolution, targeted delivery, and clinical application. Promising strategies for dealing with each challenge are reviewed in detail, and future research directions for next generation contrast-enhanced PAI are discussed.

3D printed multi-growth factor delivery patches fabricated using dual-crosslinked decellularized extracellular matrix-based hybrid inks to promote cerebral angiogenesis.

Generally, brain angiogenesis is a tightly regulated process, which scarcely occurred in the absence of specific pathological conditions. Delivery of exogenous angiogenic factors enables the induction of desired angiogenesis by stimulating neovasculature formation. However, effective strategies of mimicking the angiogenesis process with exogenous factors have not yet been fully explored. Herein, we develop a 3D printed spatiotemporally compartmentalized cerebral angiogenesis inducing (SCAI) hydrogel patch, releasing dual angiogenic growth factors (GFs), using extracellular matrix-based hybrid inks. We introduce a new hybrid biomaterial-based ink for printing patches through dual crosslinking mechanisms: Chemical crosslinking with aza-Michael addition reaction with combining methacrylated hyaluronic acid (HAMA) and vascular-tissue-derived decellularized extracellular matrix (VdECM), and thermal crosslinking of VdECM. 3D printing technology, a useful approach with fabrication versatility with customizable systems and multiple biomaterials, is adopted to print three-layered hydrogel patch with spatially separated dual GFs as outer- and inner-layers that provide tunable release profiles of multiple GFs and fabrication versatility. Consequently, these layers of the patch spatiotemporally separated with dual GFs induce excellent neovascularization in the brain area, monitored by label-free photoacoustic microscopy in vivo. The developed multi-GFs releasing patch may offer a promising therapeutic approach of spatiotemporal drugs releasing such as cerebral ischemia, ischemic heart diseases, diabetes, and even use as vaccines. STATEMENT OF SIGNIFICANCE: Effective strategies of mimicking the angiogenesis process with exogenous factors have not yet been fully explored. In this study, we develop a 3D printed spatiotemporally compartmentalized cerebral angiogenesis inducing (SCAI) hydrogel patch, releasing dual angiogenic growth factors (GFs) using extracellular matrix-based hybrid inks. We introduce a new hybrid biomaterial-based ink through dual crosslinking mechanisms: Chemical crosslinking with aza-Michael addition, and thermal crosslinking. 3D printing technology is adopted to print three-layered hydrogel patch with spatially separated dual GFs as outer- and inner-layers that provide tunable release profiles of multiple GFs and fabrication versatility. Consequently, these layers of the patch spatiotemporally separated with dual GFs induce excellent neovascularization in the brain area, monitored by photoacoustic microscopy in vivo.

Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy.

Simultaneous point-by-point raster scanning of optical and acoustic beams has been widely adapted to high-speed photoacoustic microscopy (PAM) using a water-immersible microelectromechanical system or galvanometer scanner. However, when using high-speed water-immersible scanners, the two consecutively acquired bidirectional PAM images are misaligned with each other because of unstable performance, which causes a non-uniform time interval between scanning points. Therefore, only one unidirectionally acquired image is typically used; consequently, the imaging speed is reduced by half. Here, we demonstrate a scanning framework based on a deep neural network (DNN) to correct misaligned PAM images acquired via bidirectional raster scanning. The proposed method doubles the imaging speed compared to that of conventional methods by aligning nonlinear mismatched cross-sectional B-scan photoacoustic images during bidirectional raster scanning. Our DNN-assisted raster scanning framework can further potentially be applied to other raster scanning-based biomedical imaging tools, such as optical coherence tomography, ultrasound microscopy, and confocal microscopy.

A voting-based ensemble feature network for semiconductor wafer defect classification.

Semiconductor wafer defects severely affect product development. In order to reduce the occurrence of defects, it is necessary to identify why they occur, and it can be inferred by analyzing the patterns of defects. Automatic defect classification (ADC) is used to analyze large amounts of samples. ADC can reduce human resource requirements for defect inspection and improve inspection quality. Although several ADC systems have been developed to identify and classify wafer surfaces, the conventional ML-based ADC methods use numerous image recognition features for defect classification and tend to be costly, inefficient, and time-consuming. Here, an ADC technique based on a deep ensemble feature framework (DEFF) is proposed that classifies different kinds of wafer surface damage automatically. DEFF has an ensemble feature network and the final decision network layer. The feature network learns features using multiple pre-trained convolutional neural network (CNN) models representing wafer defects and the ensemble features are computed by concatenating these features. The decision network layer decides the classification labels using the ensemble features. The classification performance is further enhanced by using a voting-based ensemble learning strategy in combination with the deep ensemble features. We show the efficacy of the proposed strategy using the real-world data from SK Hynix.

Deep learning acceleration of multiscale superresolution localization photoacoustic imaging.

A superresolution imaging approach that localizes very small targets, such as red blood cells or droplets of injected photoacoustic dye, has significantly improved spatial resolution in various biological and medical imaging modalities. However, this superior spatial resolution is achieved by sacrificing temporal resolution because many raw image frames, each containing the localization target, must be superimposed to form a sufficiently sampled high-density superresolution image. Here, we demonstrate a computational strategy based on deep neural networks (DNNs) to reconstruct high-density superresolution images from far fewer raw image frames. The localization strategy can be applied for both 3D label-free localization optical-resolution photoacoustic microscopy (OR-PAM) and 2D labeled localization photoacoustic computed tomography (PACT). For the former, the required number of raw volumetric frames is reduced from tens to fewer than ten. For the latter, the required number of raw 2D frames is reduced by 12 fold. Therefore, our proposed method has simultaneously improved temporal (via the DNN) and spatial (via the localization method) resolutions in both label-free microscopy and labeled tomography. Deep-learning powered localization PA imaging can potentially provide a practical tool in preclinical and clinical studies requiring fast temporal and fine spatial resolutions.

Properties of Fe-Si Alloy Anode for Lithium-Ion Battery Synthesized Using Mechanical Milling.

Silicon (Si)-based anode materials can increase the energy density of lithium (Li)-ion batteries owing to the high weight and volume capacity of Si. However, their electrochemical properties rapidly deteriorate due to large volume changes in the electrode resulting from repeated charging and discharging. In this study, we manufactured structurally stable Fe-Si alloy powders by performing high-energy milling for up to 24 h through the reduction of the Si phase size and the formation of the α-FeSi2 phase. The cause behind the deterioration of the electrochemical properties of the Fe-Si alloy powder produced by over-milling (milling for an increased time) was investigated. The 12 h milled Fe-Si alloy powder showed the best electrochemical properties. Through the microstructural analysis of the Fe-Si alloy powders after the evaluation of half/full coin cells, powder resistance tests, and charge/discharge cycles, it was found that this was due to the low electrical conductivity and durability of ß-FeSi2. The findings provide insight into the possible improvements in battery performance through the commercialization of Fe-Si alloy powders produced by over-milling in a mechanical alloying process.

Importance of Chemical Distortion on the Hysteretic Oxygen Capacity in Li-Excess Layered Oxides.

Nonhysteretic redox capacity is a critical factor in achieving high energy density without energy loss during cycling for rechargeable battery electrodes, which has been considered a major challenge in oxygen redox (OR) for Li-excess layered oxide cathodes for lithium-ion batteries (LIBs). Until recently, transition metal migration into the Li metal layer and the formation of O-O dimers have been considered major factors affecting hysteretic oxygen capacity. However, Li-excess layered oxides, particularly Ru oxides, exhibit peculiar voltage hysteresis that cannot be sufficiently described by only these factors. Therefore, this study aims to unlock the critical impeding factors in restraining the non-polarizing oxygen capacity of Li-excess layered oxides (herein, Li2RuO3) that exhibit reversible OR reactions. First, Li2RuO3 undergoes an increase in the chemical potential fluctuation as both the thermodynamic material instability and vacancy content increase. Second, the chemical compression of O-O bonds occurs at the early stage of the OR reaction (0.5 ≤ x ≤ 0.75) for Li1-xRu0.5O1.5, leading to flexible voltage hysteresis. Finally, in the range of 0.75 ≤ x ≤ 1.0, for Li1-xRu0.5O1.5, the formation of an O(2p)-O(2p)* antibonding state derived from the structural distortion of the RuO6 octahedron leads to the irreversibility of the OR reaction and enhanced voltage hysteresis. Consequently, our study unlocks the new decisive factor, namely, the structural distortion inducing the O(2p)-O(2p)* antibonding state, of the hysteretic oxygen capacity and provides insights into enabling the full potential of the OR reaction for Li-excess layered oxides for advanced LIBs.

Analysis of Platen Superheater Tube Degradation in Thermal Power Plants via Destructive/Non-Destructive Characteristic Evaluation.

Coal-fired power plants operating under Korea's standard supercritical pressure operate in a high-temperature environment, with steam temperatures reaching 540 °C. A standard coal-fired power plant has a 30-year design life, and lifespan diagnosis is performed on facilities that have operated for more than 100,000 h or 20 years. Visual inspection, thickness measurements, and hardness measurements in the field are used to assess the degree of material degradation at the time of diagnosis. In this study, aging degradation was assessed using an electromagnetic acoustic transducer to measure the change in transverse ultrasonic propagation speed, and the results were compared to microstructural analysis and tensile test results. Based on the experimental results, it was found that the boiler tube exposed to a high-temperature environment during long-term boiler operation was degraded and damaged, the ultrasonic wave velocity was reduced, and the microstructural grains were coarsened. It was also confirmed through tensile testing that the tensile and yield strengths increased with degradation. Our findings prove that the degree of change in mechanical properties as a function of the material's degradation state is proportional to the change in ultrasonic wave velocity.

Integrated deep learning framework for accelerated optical coherence tomography angiography.

Label-free optical coherence tomography angiography (OCTA) has become a premium imaging tool in clinics to obtain structural and functional information of microvasculatures. One primary technical drawback for OCTA, however, is its imaging speed. The current protocols require high sampling density and multiple acquisitions of cross-sectional B-scans to form one image frame, resulting in low acquisition speed. Recently, deep learning (DL)-based methods have gained attention in accelerating the OCTA acquisition process. They achieve faster acquisition using two independent reconstructing approaches: high-quality angiograms from a few repeated B-scans and high-resolution angiograms from undersampled data. While these approaches have shown promising results, they provide limited solutions that only partially account for the OCTA scanning mechanism. Herein, we propose an integrated DL method to simultaneously tackle both factors and further enhance the reconstruction performance in speed and quality. We designed an end-to-end deep neural network (DNN) framework with a two-staged adversarial training scheme to reconstruct fully-sampled, high-quality (8 repeated B-scans) angiograms from their corresponding undersampled, low-quality (2 repeated B-scans) counterparts by successively enhancing the pixel resolution and the image quality. Using an in-vivo mouse brain vasculature dataset, we evaluate our proposed framework through quantitative and qualitative assessments and demonstrate that our method can achieve superior reconstruction performance compared to the conventional means. Our DL-based framework can accelerate the OCTA imaging speed from 16 to 256[Formula: see text] while preserving the image quality, thus enabling a convenient software-only solution to enhance preclinical and clinical studies.

Detection of micro inclusions in steel sheets using high-frequency ultrasound speckle analysis.

With the increasing need for steel sheet quality assurance, the detection of micro-scaled inclusions in steel sheets has become critical. Many techniques have been explored to detect inclusions, e.g., visual inspection, radiography, magnetic testing, and ultrasound. Among these methods, ultrasound (US) is the most commonly used non-destructive testing (NDT) method due to its ease of use and deep penetration depth. However, ultrasound currently cannot be used for detecting the micro-scaled inclusions due to low spatial resolution, e.g., less than 30 µm, which are the key important factors causing the cracks in the high-quality steel sheets. Here, we demonstrate a high-resolution US imaging (USI) using high-frequency US transducers to image micro inclusions in steel sheets. Our system utilizes through-transmission USI and identifies ultrasound scattering produced by the inclusions. We first ultrasonically imaged the artificial flaws induced by the laser on the steel sheet surface for validating the system. We then imaged the real inclusions in the steel sheets formed during manufacturing processes and analyzed them to derive quantitative parameters related to the number of micro-scaled inclusions. Our results confirm that inclusions less than 30 µm can be identified using our high-resolution USI modality and has the potential to be used as an effective tool for quality assurance of the steel sheets.

Perovskite solar cells with atomically coherent interlayers on SnO2 electrodes.

In perovskite solar cells, the interfaces between the perovskite and charge-transporting layers contain high concentrations of defects (about 100 times that within the perovskite layer), specifically, deep-level defects, which substantially reduce the power conversion efficiency of the devices1-3. Recent efforts to reduce these interfacial defects have focused mainly on surface passivation4-6. However, passivating the perovskite surface that interfaces with the electron-transporting layer is difficult, because the surface-treatment agents on the electron-transporting layer may dissolve while coating the perovskite thin film. Alternatively, interfacial defects may not be a concern if a coherent interface could be formed between the electron-transporting and perovskite layers. Here we report the formation of an interlayer between a SnO2 electron-transporting layer and a halide perovskite light-absorbing layer, achieved by coupling Cl-bonded SnO2 with a Cl-containing perovskite precursor. This interlayer has atomically coherent features, which enhance charge extraction and transport from the perovskite layer, and fewer interfacial defects. The existence of such a coherent interlayer allowed us to fabricate perovskite solar cells with a power conversion efficiency of 25.8 per cent (certified 25.5 per cent)under standard illumination. Furthermore, unencapsulated devices maintained about 90 per cent of their initial efficiency even after continuous light exposure for 500 hours. Our findings provide guidelines for designing defect-minimizing interfaces between metal halide perovskites and electron-transporting layers.

Metallophobic Coatings to Enable Shape Reconfigurable Liquid Metal Inside 3D Printed Plastics.

Liquid metals adhere to most surfaces despite their high surface tension due to the presence of a native gallium oxide layer. The ability to change the shape of functional fluids within a three-dimensional (3D) printed part with respect to time is a type of four-dimensional printing, yet surface adhesion limits the ability to pump liquid metals in and out of cavities and channels without leaving residue. Rough surfaces prevent adhesion, but most methods to roughen surfaces are difficult or impossible to apply on the interior of parts. Here, we show that silica particles suspended in an appropriate solvent can be injected inside cavities to coat the walls. This technique creates a transparent, nanoscopically rough (10-100 nm scale) coating that prevents adhesion of liquid metals on various 3D printed plastics and commercial polymers. Liquid metals roll and even bounce off treated surfaces (the latter occurs even when dropped from heights as high as 70 cm). Moreover, the coating can be removed locally by laser ablation to create selective wetting regions for metal patterning on the exterior of plastics. To demonstrate the utility of the coating, liquid metals were dynamically actuated inside a 3D printed channel or chamber without pinning the oxide, thereby demonstrating electrical circuits that can be reconfigured repeatably.

Ultrasonic nonlinearity parameter in uniaxial stress condition.

The ultrasonic nonlinearity parameter derived for one-dimensional propagation of a longitudinal wave in an isotropic material has been considered useful in the evaluation of material degradation. To demonstrate this, many researchers have reported on the correlation with the yield strength obtained from a tensile test. However, there is an essential issue with this procedure - which is that the ultrasonic nonlinearity parameter is derived in a state where the lateral strain is restrained, whereas the tensile test to measure the yield strength is carried out under uniaxial stress conditions, where lateral deformation is free. In this study, to address this issue, the authors have defined the ultrasonic nonlinearity parameter under uniaxial stress conditions which were the same as the tensile test, and showed that the correlation with the yield strength was higher than the currently used ultrasonic nonlinearity parameter. To verify the validity of the proposed ultrasonic nonlinearity parameter, experiments were carried out for Al6061-T6 alloy specimens heat-treated with different aging times. Results showed that the proposed ultrasonic nonlinearity parameter exhibited a much higher correlation with yield strength than the currently used nonlinearity parameter.

Super Wide-Field Photoacoustic Microscopy of Animals and Humans In Vivo.

Acoustic-resolution photoacoustic micro-scopy (AR-PAM) is an emerging biomedical imaging modality that combines superior optical sensitivity and fine ultrasonic resolution in an optical quasi-diffusive regime (~1-3 mm in tissues). AR-PAM has been explored for anatomical, functional, and molecular information in biological tissues. Heretofore, AR-PAM systems have suffered from a limited field-of-view (FOV) and/or slow imaging speed, which have precluded them from routine preclinical and clinical applications. Here, we demonstrate an advanced AR-PAM system that overcomes both limitations of previous AR-PAM systems. The new AR-PAM system demonstrates a super wide-field scanning that utilized a 1-axis water-proofing microelectromechanical systems (MEMS) scanner integrated with two linear stepper motor stages. We achieved an extended FOV of 36 ×80 mm2 by mosaicking multiple volumetric images of 36 ×2.5 mm2 with a total acquisition time of 224 seconds. For one volumetric data (i.e., 36 ×2.5 mm2), the B-scan imaging speed over the short axis (i.e., 2.5 mm) was 83 Hz in humans. The 3D volumetric image was also provided by using MEMS mirror scanning along the X-axis and stepper-motor scanning along the Y-axis. The super-wide FOV mosaic image was realized by registering and merging all individual volumetric images. Finally, we obtained multi-plane whole-body in-vivo PA images of small animals, illustrating distinct multi-layered structures including microvascular networks and internal organs. Importantly, we also visualized microvascular networks in human fingers, palm, and forearm successfully. This advanced MEMS-AR-PAM system could potentially enable hitherto not possible wide preclinical and clinical applications.

Super-resolution localization photoacoustic microscopy using intrinsic red blood cells as contrast absorbers.

Photoacoustic microscopy (PAM) has become a premier microscopy tool that can provide the anatomical, functional, and molecular information of animals and humans in vivo. However, conventional PAM systems suffer from limited temporal and/or spatial resolution. Here, we present a fast PAM system and an agent-free localization method based on a stable and commercial galvanometer scanner with a custom-made scanning mirror (L-PAM-GS). This novel hardware implementation enhances the temporal resolution significantly while maintaining a high signal-to-noise ratio (SNR). These improvements allow us to photoacoustically and noninvasively observe the microvasculatures of small animals and humans in vivo. Furthermore, the functional hemodynamics, namely, the blood flow rate in the microvasculature, is successfully monitored and quantified in vivo. More importantly, thanks to the high SNR and fast B-mode rate (500 Hz), by localizing photoacoustic signals from captured red blood cells without any contrast agent, unresolved microvessels are clearly distinguished, and the spatial resolution is improved by a factor of 2.5 in vivo. L-PAM-GS has great potential in various fields, such as neurology, oncology, and pathology.

Review on practical photoacoustic microscopy.

Photoacoustic imaging (PAI) has many interesting advantages, such as deep imaging depth, high image resolution, and high contrast to intrinsic and extrinsic chromophores, enabling morphological, functional, and molecular imaging of living subjects. Photoacoustic microscopy (PAM) is one form of the PAI inheriting its characteristics and is useful in both preclinical and clinical research. Over the years, PAM systems have been evolved in several forms and each form has its relative advantages and disadvantages. Thus, to maximize the benefits of PAM for a specific application, it is important to configure the PAM system optimally by targeting a specific application. In this review, we provide practical methods for implementing a PAM system to improve the resolution, signal-to-noise ratio (SNR), and imaging speed. In addition, we review the preclinical and the clinical applications of PAM and discuss the current challenges and the scope for future developments.

Liquid Metal Nanoparticles as Initiators for Radical Polymerization of Vinyl Monomers.

Sonication of gallium or gallium-based liquid metals in an aqueous solution of vinyl monomers leads to rapid free radical polymerization (FRP), without the need for conventional molecular initiators. Under ambient conditions, a passivating native oxide separates these metals from solution and renders the metal effectively inert. However, sonication generates liquid metal nanoparticles (LMNPs) of ∼100 nm diameter and thereby increases the surface area of the metal. The exposed metal initiates polymerization, which proceeds via a FRP mechanism and yields high molecular weight polymers that can form physical gels. Spin trapping EPR reveals the generation of free radicals. Time-of-flight secondary ion mass spectrometry measurements confirm direct polymer bonding to gallium, verifying the formation of surface-anchored polymer grafts. The grafted polymers can modify the interfacial properties, that is, the preference of the metal particles to disperse in aqueous versus organic phases. The polymer can also be degrafted and isolated from the particles using strong acid or base. The concept of physically disrupting passivated metal surfaces offers new routes for surface-initiated polymerization and has implications for surface modification, reduction reactions, and fabrication of mechanically responsive materials.

Dependence of nonlinear ultrasonic characteristic on second-phase precipitation in heat-treated Al 6061-T6 alloy.

It is well known that nonlinear ultrasound is sensitive to certain microstructural features in materials such as dislocations and precipitates. This paper investigates the dependence of the nonlinear ultrasonic characteristic on MgnSim precipitation in heat-treated Al 6061-T6 alloy specimens. The specimens were heat-treated at a constant temperature of 220°C for different exposure times up to 6000min. The accuracy of the ultrasonic measurement setup that determines the absolute ultrasonic nonlinearity parameter ßu was first validated. The nonlinearity parameter and the yield strength were measured for each of the artificially aged specimens. The experimental results show fluctuations in the nonlinearity parameter and yield strength over the aging time, but with an interesting correlation between the nonlinearity parameter and the yield strength over the aging time. Microstructural observations confirmed that those fluctuations are due to the formation and evolution of precipitates that occurs in a unique precipitation sequence in this alloy. These results suggest that the nonlinear ultrasonic measurement can be useful for monitoring second phase precipitation and related mechanical properties in the Al 6061-T6 alloy.